Shonan Rotation Averaging: Global Optimality by Surfing SO(p) <sup>n</sup>
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2008.02737.pdf
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Accepted version
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404.56 KB
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33f8adb7dbf22140f68093ff0828054e
Author(s)
Dellaert, Frank
Rosen, David Matthew
Wu, Jing
Mahony, Robert
Carlone, Luca
Date Issued
November 2020
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
Dellaert, Frank, Rosen, David Matthew, Wu, Jing, Mahony, Robert and Carlone, Luca. 2020. "Shonan Rotation Averaging: Global Optimality by Surfing SO(p) n." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12351 LNCS.
Version
Author's final manuscript
Abstract
© 2020, Springer Nature Switzerland AG. Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally optimal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations using manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from-motion pipelines. Our method thus preserves the speed and scalability of current SFM methods, while recovering globally optimal solutions.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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DOI of Published Version
https://doi.org/10.1007/978-3-030-58539-6_18
